\select@language {english}
\contentsline {chapter}{Nomenclature}{v}{chapter*.2}
\contentsline {chapter}{\numberline {1}Introduction}{1}{chapter.1}
\contentsline {section}{\numberline {1.1}ASR System Architecture}{2}{section.1.1}
\contentsline {section}{\numberline {1.2}Formal Description of ASR}{4}{section.1.2}
\contentsline {section}{\numberline {1.3}Challenges in ASR}{5}{section.1.3}
\contentsline {section}{\numberline {1.4}Discriminative Training}{6}{section.1.4}
\contentsline {chapter}{\numberline {2}Hidden Markov Model}{10}{chapter.2}
\contentsline {section}{\numberline {2.1}Definition of a HMM}{10}{section.2.1}
\contentsline {section}{\numberline {2.2}Basic Problems for HMM}{12}{section.2.2}
\contentsline {subsection}{\numberline {2.2.1}Evaluation: Forward-Backward Procedure}{12}{subsection.2.2.1}
\contentsline {subsection}{\numberline {2.2.2}Decoding: Viterbi Algorithm}{15}{subsection.2.2.2}
\contentsline {subsection}{\numberline {2.2.3}Training: The Baum-Welch Algorithm}{16}{subsection.2.2.3}
\contentsline {section}{\numberline {2.3}Training Criteria of HMM}{18}{section.2.3}
\contentsline {chapter}{\numberline {3}Discriminative Training}{20}{chapter.3}
\contentsline {section}{\numberline {3.1}Introduction}{20}{section.3.1}
\contentsline {section}{\numberline {3.2}Maximum Mutual Information}{21}{section.3.2}
\contentsline {section}{\numberline {3.3}Minimum Classification Error}{22}{section.3.3}
\contentsline {section}{\numberline {3.4}Minimum Phone Error}{23}{section.3.4}
\contentsline {section}{\numberline {3.5}Large Margin}{25}{section.3.5}
\contentsline {subsection}{\numberline {3.5.1}Decision Rule}{25}{subsection.3.5.1}
\contentsline {subsection}{\numberline {3.5.2}Margin Maximization}{26}{subsection.3.5.2}
\contentsline {section}{\numberline {3.6}Optimization Methods}{27}{section.3.6}
\contentsline {subsection}{\numberline {3.6.1}Extended Baum-Welch algorithm}{27}{subsection.3.6.1}
\contentsline {subsection}{\numberline {3.6.2}Gradient Descent}{28}{subsection.3.6.2}
\contentsline {subsection}{\numberline {3.6.3}Quickprop Algorithm}{29}{subsection.3.6.3}
\contentsline {subsection}{\numberline {3.6.4}Rprop}{30}{subsection.3.6.4}
\contentsline {section}{\numberline {3.7}Multiple Layer Perceptron}{31}{section.3.7}
\contentsline {subsection}{\numberline {3.7.1}Basic Structure}{31}{subsection.3.7.1}
\contentsline {subsection}{\numberline {3.7.2}Motivations}{33}{subsection.3.7.2}
\contentsline {subsection}{\numberline {3.7.3}Discriminative nature of MLP inference}{34}{subsection.3.7.3}
\contentsline {subsection}{\numberline {3.7.4}Estimating HMM Emission Probabilities with NN}{34}{subsection.3.7.4}
\contentsline {chapter}{\numberline {4}Weighted Finite State Transducer}{37}{chapter.4}
\contentsline {section}{\numberline {4.1}Introduction}{37}{section.4.1}
\contentsline {section}{\numberline {4.2}WFST Overview}{38}{section.4.2}
\contentsline {subsection}{\numberline {4.2.1}Semi-ring and Weights}{38}{subsection.4.2.1}
\contentsline {subsection}{\numberline {4.2.2}Weighted Finite State Transducer}{39}{subsection.4.2.2}
\contentsline {section}{\numberline {4.3}Key Algorithms}{40}{section.4.3}
\contentsline {subsection}{\numberline {4.3.1}Composition}{40}{subsection.4.3.1}
\contentsline {subsection}{\numberline {4.3.2}Determinization}{41}{subsection.4.3.2}
\contentsline {subsection}{\numberline {4.3.3}Minimization}{43}{subsection.4.3.3}
\contentsline {section}{\numberline {4.4}WFST Representations of Knowledge Sources}{43}{section.4.4}
\contentsline {subsection}{\numberline {4.4.1}HMM Transducer}{43}{subsection.4.4.1}
\contentsline {subsection}{\numberline {4.4.2}Context Dependency}{45}{subsection.4.4.2}
\contentsline {subsection}{\numberline {4.4.3}Pronunciation Lexicon}{47}{subsection.4.4.3}
\contentsline {subsection}{\numberline {4.4.4}Language Model}{47}{subsection.4.4.4}
\contentsline {subsection}{\numberline {4.4.5}Integration of Knowledge Sources}{48}{subsection.4.4.5}
\contentsline {chapter}{\numberline {5}Preliminary Results And Future Work}{50}{chapter.5}
\contentsline {section}{\numberline {5.1}Preliminary Experimental Results}{50}{section.5.1}
\contentsline {subsection}{\numberline {5.1.1}Database Description}{50}{subsection.5.1.1}
\contentsline {subsection}{\numberline {5.1.2}Phone Recognition Results on WSJ0}{51}{subsection.5.1.2}
\contentsline {subsubsection}{\numberline {5.1.2.1}Monophone Based Phone Recognition}{52}{subsubsection.5.1.2.1}
\contentsline {subsubsection}{\numberline {5.1.2.2}Triphone Based Phone Recognition}{53}{subsubsection.5.1.2.2}
\contentsline {subsection}{\numberline {5.1.3}Word Recognition Results on WSJ0}{55}{subsection.5.1.3}
\contentsline {section}{\numberline {5.2}Discussions}{56}{section.5.2}
\contentsline {subsection}{\numberline {5.2.1}Training Criteria for NNs}{56}{subsection.5.2.1}
\contentsline {subsection}{\numberline {5.2.2}Limitations of HMM System Based Discriminative Training Criteria}{58}{subsection.5.2.2}
\contentsline {subsection}{\numberline {5.2.3}Advantages of NN Acoustic Modelling}{58}{subsection.5.2.3}
\contentsline {section}{\numberline {5.3}Future Work}{59}{section.5.3}
\contentsline {subsection}{\numberline {5.3.1}NNs For Feature Transformation}{59}{subsection.5.3.1}
\contentsline {subsection}{\numberline {5.3.2}Sequence Classification Based NN Training Criteria}{60}{subsection.5.3.2}
\contentsline {chapter}{\numberline {6}Conclusions}{62}{chapter.6}
\contentsline {chapter}{References}{67}{chapter*.3}
